Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Explaining Preferences by Multiple Patterns in Voters’ Behavior
Authors: Sonja Kraiczy, Edith Elkind
IJCAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,280 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,505 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we study the complexity of deciding whether voters preferences can be explained in this manner. For k = 2, we use the technique developed by Yang [2020] in the context of single-peaked preferences to obtain a polynomial-time algorithm for several domains: value-restricted preferences, group-separable preferences, and a natural subdomain of group-separable preferences, namely, caterpillar group-separable preferences. For k 3, the problem is known to be hard for single-peaked preferences; we show that this is also the case for value-restricted and group-separable preferences. |
| Researcher Affiliation | Academia | Sonja Kraiczy and Edith Elkind Department of Computer Science, University of Oxford EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithmic steps in paragraph form, such as in Section 3 (Partitioning Voters into Two Groups) and Section 5 (Group-separability on a Caterpillar), but does not present them as structured pseudocode blocks or clearly labeled algorithm figures. |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical, focusing on computational complexity and mathematical characterizations of preference profiles. It does not use or refer to any empirical datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data; therefore, it does not mention training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setups or report on empirical results that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and complexity analysis. It does not describe any computational experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments. Therefore, it does not include details about an experimental setup, such as hyperparameters or system-level training settings. |